Abstracts
Scalability of Multicast Based Synchronization Methods
Consistent maintenance of distributed data is important in
application areas like groupware and for runtime support for
parallel computing. We examine the performance of different
multicast based methods for maintaining the consistency of
distributed data depending on the network topology and
concurrency.
Some amount of data is kept distributed or replicated on some
or all nodes of a distributed system. At every moment, each
instance that accesses this data must see the same
information. Updates must be delivered ordered, reliably, and
efficiently.
Our prototype software implements ordered, reliable multicasts
on top of the unreliable IP broad- or multicast with three
different methods (Master-Slave, Token Exchange on Demand,
Totem Single Ring). This paper shows measurement results for
the efficiency and scalability of the three methods in
different topologies.
The measurements confirm earlier analytical results. Totem
behaves well in large networks with many concurrent
senders. The overhead of Token on Demand and of the
Master-Slave algorithm is almost the same. Also we could not
find an indication for the often-read opinion that the
Master-Slave approach scales worse because of the central
bottleneck.
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